Salman Khalid (Ph.D.) - Accomplished Research Projects

Industrial Artificial Intelligence for Intelligent machines and manufacturing digitization

Sponsored as part of the research project by Ministry of Trade, Industry and Energy

Key tasks:

Data acquisition and data pre-processing from the industrial 6 D.O.F robot.
Overcome the data imbalance problem by usage of transfer learning and area-metric-based sampling methods.
Utilization of Machine learning and deep learning algorithms for Intelligent Fault Diagnosis of Robotic Strain Wave Gear Reducer
Employ explainable AI to explain deep learning model results.

Development of fault detection and diagnosis system in Intelligent digital power plant

Sponsored as part of the research project (R17GA08) by Korea Electric Power Corporation

Key tasks:

Data acquisition and data preprocessing
Boiler water wall tube leakage detection and turbine motor fault detection case studies
Optimal sensor selection via machine learning
Utilization of Machine learning and deep learning algorithms
Physics-based data generation
Data augmentation

Research and development of risk evaluation method for aircraft composite structures

Sponsored by Korea Aero Technology Research Institute.

Key tasks:
Development of Finite Element based Fatigue crack propagation approach
Implementation on simple composite Double cantilever beam specimen
Implementation of fatigue crack propagation approach in Complex aircraft tail wing structure
Sensitivity analysis and design of experiments by using Latin hypercube selection
Inspection interval optimization

Development of a Sterilization System for Medical Waste for Infection Optimized by Hospital Size

Sponsored by Korea Environmental Industry and Technology Institute (KEITI) and funded by the Ministry of Environment in 2021 (No.RE202101609).

Key tasks:

Finite element modeling of the shredder blade
Von mises stress analysis
Topology optimization for optimum design
Fatigue life modeling for nominal optimized blade design
Sensitivity analysis of the design parameters

Real-time defect detection of smart composite structures using data augmentation and artificial intelligence

Sponsored by the Basic Science Research Program through the National Research Foundation of Korea (NRF2020R1A2C1006613), funded by the Ministry of Education.

Key tasks:

Data acquisition from mathematical models and experimentation
Obtain Structural response from composite laminated structures
Deep learning models for autonomous feature extraction
Performance evaluation of extracted features through machine learning classification